Allen School researchers are at the forefront of exciting developments in AI spanning machine learning, computer vision, natural language processing, robotics and more.
We cultivate a deeper understanding of the science and potential impact of rapidly evolving technologies, such as large language models and generative AI, while developing practical tools for their ethical and responsible application in a variety of domains — from biomedical research and disaster response, to autonomous vehicles and urban planning.
Groups & Labs
UW Reality Lab
The UW Reality Lab advances the state of the art in virtual and augmented reality by developing new technologies and applications, educating the next generation of researchers and technologists, and supporting robust collaborations with industry.
Mostafavi Lab
The Mostafavi Lab develops machine learning and statistical methods that combine evidence across multiple types of molecular/genomics data and disentangle spurious from meaningful correlations for new insights into mechanisms of health and disease.
Faculty Members
Centers & Initiatives
TCAT harnesses the power of open-source technology to develop, translate, and deploy accessible technologies, and then sustain them in the hands of communities. Housed by the Paul G. Allen School for Computer Science & Engineering, TCAT centers the experience of people with disabilities as a lens for improving design & engineering, through participatory design practices, tooling and capacity building.
Computing for the Environment (CS4Env) at the University of Washington supports novel collaborations across the broad fields of environmental sciences and computer science & engineering. The initiative engages environmental scientists and engineers, computer scientists and engineers, and data scientists in using advanced technologies, methodologies and computing resources to accelerate research that addresses pressing societal challenges related to climate change, pollution, biodiversity and more.
Highlights
UW News
In a paper published in the journal Nature, a team of Allen School and Ai2 researchers unveiled OpenScholar, a system that can cite scientific papers as accurately as human experts and incorporate new research after it has been trained.
Allen School News
The Institute of Electrical and Electronics Engineers (IEEE) recognized Kemelmacher-Shlizerman for her “contributions to face, body, and clothing modeling from large image collections,” including pioneering virtual try-on tools and bringing the technology to the mainstream.
Allen School News
A team of Allen School and Ai2 researchers were recognized for developing an efficient, scalable system for indexing petabyte-level text corpora with minimal storage overhead to better understand the data on which large language models are trained.